// gradient registerer to instantiate a GradientFunction.
struct ForwardOperation {
 public:
  string op_name;
  std::vector<AbstractTensorHandle*> inputs;
  std::vector<AbstractTensorHandle*> outputs;
  std::vector<int64_t> skip_input_indices;
  AttrBuilder attrs;
};

using GradientFunctionFactory =
    std::function<GradientFunction*(const ForwardOperation& op)>;

limitations under the License.
==============================================================================*/
#ifndef TENSORFLOW_C_EAGER_C_API_TEST_UTIL_H_
#define TENSORFLOW_C_EAGER_C_API_TEST_UTIL_H_

#include <vector>

#include "tensorflow/c/eager/c_api.h"
#include "tensorflow/c/eager/c_api_experimental.h"
#include "tensorflow/c/tf_datatype.h"
#include "tensorflow/core/platform/logging.h"
#include "tensorflow/core/platform/tstring.h"

// original framework of destruction, and this context will be deleted also.
struct TfDlManagedTensorCtx {
  TensorReference reference;
  std::vector<int64_t> shape;
  std::vector<int64_t> strides;
  DLManagedTensor tensor;

  explicit TfDlManagedTensorCtx(const TensorReference& ref) : reference(ref) {}
};

// Gets tensor from eager tensor handle.

        }
        return out;
    }

    @Test
    @DisplayName("getNTHash: known vector for 'password'")
    void testGetNTHash_knownVector() {
        // Arrange
        String password = "password";
        // Known NT hash for "password" (UTF-16LE MD4)
        // This is a well-known test vector: password -> 8846F7EAEE8FB117AD06BDD830B7586C
        byte[] expected = hex("8846F7EAEE8FB117AD06BDD830B7586C");

//     system.xml. Vendors can work with Veeam Product Management and the Alliances team on getting approval to integrate
//     specific system recommendations based on current support case statistics and storage performance possibilities.
//     Vendors might change the settings based on the configuration and scale out of the solution (more storage nodes =>

        assertArrayEquals(result1, result2);
    }

    @Test
    void testHMACT64WithKnownTestVector() throws NoSuchAlgorithmException {
        // Test with known test vector
        // Using test vector from RFC 2104 adapted for HMACT64
        byte[] key = new byte[16];
        Arrays.fill(key, (byte) 0x0b);
        byte[] data = "Hi There".getBytes();

==============================================================================*/

#include "tensorflow/c/eager/c_api_experimental.h"

#include <cstdint>
#include <memory>
#include <utility>
#include <vector>

#include "absl/container/flat_hash_map.h"
#include "absl/strings/match.h"
#include "absl/time/time.h"
#include "tensorflow/c/c_api.h"
#include "tensorflow/c/eager/c_api_internal.h"

}

inline absl::Status AbstractOperation::SetAttrStringList(
    const char* attr_name, absl::Span<string const> values) {
  std::vector<const char*> raw_strs;
  std::vector<size_t> lengths;
  raw_strs.reserve(values.size());
  lengths.reserve(values.size());
  for (const auto& s : values) {
    raw_strs.emplace_back(s.data());
    lengths.emplace_back(s.size());

    } else {
      TFE_OpAddInput(op, input, s);
    }
    if (TF_GetCode(s) != TF_OK) return;
  }
  std::vector<TFE_TensorHandle*> op_outputs(*num_outputs);
  TFE_Execute(op, op_outputs.data(), num_outputs, s);
  TFE_DeleteOp(op);
  if (TF_GetCode(s) != TF_OK) return;
  std::vector<TFE_TensorHandle*> unwrapped_outputs;
  unwrapped_outputs.reserve(op_outputs.size());
  for (auto* handle : op_outputs) {

		}
	}
	if opd, ok := riscv64SpecialOperand[name]; ok {
		return opd
	}
	return riscv.SPOP_END
}

// RISCV64ValidateVectorType reports whether the given configuration is a
// valid vector type.
func RISCV64ValidateVectorType(vsew, vlmul, vtail, vmask int64) error {
	_, err := riscv.EncodeVectorType(vsew, vlmul, vtail, vmask)
	return err